Diagnostic value of short-term longitudinal clinical and laboratory trajectories for gram-negative bacteremia in intensive care units patients - Dilken, Olcay; Bayrak, Ahmet Dogukan; Derin, Okan; Tekbas, Ugur; Altinay, Mustafa; Yakar, Mehmet Nuri; Ergin, Bulent; Kilic Depren, Serpil (SAGE PUBLICATIONS LTD, 2026)
Early identification of gram-negative bacteremia (GN-BSI) in intensive care units (ICUs) remains challenging at the time of blood culture sampling, when clinical signs are often nonspecific and existing diagnostic approaches typically rely on single-timepoint measurements. We conducted a retrospective cohort study of adult ICU patients admitted between July 2022 and January 2024 to investigate whether short-term longitudinal patterns in routinely collected clinical and laboratory data contain diagnostically relevant information for GN-BSI. Clinical and laboratory variables were extracted at three consecutive timepoints (Day -2, -1, and 0 relative to blood culture collection), and diagnostic models incorporating this temporal information were developed using complementary statistical and machine-learning approaches. Model performance was evaluated on a held-out test set using discrimination, calibration, and decision curve analysis. Among 568 patients, models incorporating short-term longitudinal data demonstrated good and consistent discrimination for GN-BSI (AUC range 0.81-0.83). A parsimonious logistic regression model of the mean values of seven predictors achieved an AUC of 0.81, which was not significantly different from the best-performing machine learning model (MILD-SVM, AUC 0.83; bootstrap test, p = 0.728). Diagnostic performance was stable across modeling approaches, indicating robustness of the underlying signal rather than dependence on a specific algorithm. Decision curve analysis suggested a higher net benefit of model-based risk stratification compared with treat-all or treat-none strategies across clinically relevant threshold probabilities. Central venous catheter presence, prior antibiotic use, hemoglobin, creatinine, and albumin consistently emerged as influential predictors. These findings indicate that short-term longitudinal clinical trajectories contain diagnostically meaningful information for GN-BSI at the time of blood culture sampling and support further external validation and prospective evaluation prior to clinical implementation.
Keywords: bacteremia; machine learning; critical care; risk assessment; random forest; EXTERNAL VALIDATION; PREDICTION; General & Internal Medicine; Research & Experimental Medicine